Defect prediction from static code features: current results, limitations, new approaches

@article{Menzies2010DefectPF,
  title={Defect prediction from static code features: current results, limitations, new approaches},
  author={Tim Menzies and Zach Milton and Burak Turhan and Bojan Cukic and Yue Jiang and Ayse Basar Bener},
  journal={Automated Software Engineering},
  year={2010},
  volume={17},
  pages={375-407}
}
Building quality software is expensive and software quality assurance (QA) budgets are limited. Data miners can learn defect predictors from static code features which can be used to control QA resources; e.g. to focus on the parts of the code predicted to be more defective. Recent results show that better data mining technology is not leading to better defect predictors. We hypothesize that we have reached the limits of the standard learning goal of maximizing area under the curve (AUC) of the… CONTINUE READING
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